from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__  =					logging.get_logger(__name__)

lowerCamelCase__  =					{
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					'''camembert'''







 def __init__(		self				:   Tuple							, lowercase_				:   Optional[int]=30522							, lowercase_				:   str=768							, lowercase_				:   Optional[int]=12							, lowercase_				:   int=12							, lowercase_				:   str=3072							, lowercase_				:   Optional[int]="gelu"							, lowercase_				:   Dict=0.1							, lowercase_				:   Dict=0.1							, lowercase_				:   Any=512							, lowercase_				:   List[str]=2							, lowercase_				:   Any=0.02							, lowercase_				:   Optional[int]=1e-1_2							, lowercase_				:   Optional[Any]=1							, lowercase_				:   Tuple=0							, lowercase_				:   Optional[Any]=2							, lowercase_				:   List[str]="absolute"							, lowercase_				:   Optional[Any]=True							, lowercase_				:   Union[str, Any]=None							, **lowercase_				:   Dict							, )						->	Optional[int]:


   """simple docstring"""







   super().__init__(pad_token_id=lowercase_							, bos_token_id=lowercase_							, eos_token_id=lowercase_							, **lowercase_)

   _UpperCamelCase           =    vocab_size
   _UpperCamelCase           =    hidden_size
   _UpperCamelCase           =    num_hidden_layers
   _UpperCamelCase           =    num_attention_heads
   _UpperCamelCase           =    hidden_act
   _UpperCamelCase           =    intermediate_size
   _UpperCamelCase           =    hidden_dropout_prob
   _UpperCamelCase           =    attention_probs_dropout_prob
   _UpperCamelCase           =    max_position_embeddings
   _UpperCamelCase           =    type_vocab_size
   _UpperCamelCase           =    initializer_range
   _UpperCamelCase           =    layer_norm_eps
   _UpperCamelCase           =    position_embedding_type
   _UpperCamelCase           =    use_cache
   _UpperCamelCase           =    classifier_dropout




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 @property
 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Mapping[str, Mapping[int, str]]:


   """simple docstring"""







   if self.task == "multiple-choice":
     _UpperCamelCase           =    {0: "batch", 1: "choice", 2: "sequence"}
   else:
     _UpperCamelCase           =    {0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
       ])





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					['''DebertaTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DebertaForMaskedLM''',
       '''DebertaForQuestionAnswering''',
       '''DebertaForSequenceClassification''',
       '''DebertaForTokenClassification''',
       '''DebertaModel''',
       '''DebertaPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFDebertaForMaskedLM''',
       '''TFDebertaForQuestionAnswering''',
       '''TFDebertaForSequenceClassification''',
       '''TFDebertaForTokenClassification''',
       '''TFDebertaModel''',
       '''TFDebertaPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
   from .tokenization_deberta import DebertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_fast import DebertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta import (
          DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
          DebertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta import (
          TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
          TFDebertaPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




